NonAlcoholic Fatty liver is associated with obesity, and HCV infection, and is a leading cause of fibrosis, cirrhosis, and liver cancer. Lipid droplet formation (steatosis) is an underlying cause of the pathologies. However, there is considerable variation in the scoring of steatosis and fibrosis by pathologists. This proposal is for Phase II of the STTR project 1R41DK082087-01 "Automated quantification of lipid droplets in fatty liver tissue sections". In Phase I, an image analysis algorithm (Steatosis Algorithm), was developed to predict the Steatosis Score of the pathologist associated with the project, via the analysis of digital photographs obtained from hematoxylin + eosin stained human biopsies. For phase II, a collaboration is proposed between Vala Sciences Inc and a team of eminent pathologists and liver specialists to further develop the Steatosis Algorithm, and to develop algorithms to quantify inflammation, and fibrosis. For this purpose we propose to scan extensive sets of slides with an automated digital slide scanner, to provide thousands of digital images for use in training the algorithms. Slide sets to be scanned include: 1) slides from the HALT-C clinical trial in which pegylated interferon was tested for possible therapeutic effects against HCV in a longitudinal study, 2) slides from patients with both Alcoholic and NonAlcoholic fatty liver and Steatohepatitis previously collected and maintained by the pathologists and physicians associated with this project, and 3) mouse liver slides obtained in studies of liver metabolism. The algorithms will provide an objective assessment of steatosis, inflammation, and fibrosis, for liver samples obtained from both humans and animal models of fatty liver, and will be used in both the clinical and research areas. PUBLIC HEALTH RELEVANCE: "Fatty liver disease" is a condition that affects a high proportion of the US population, particularly people that are overweight or obese, or are infected with Hepatitis C virus. Fatty liver is characterized by the occurrence of fat droplets within the cells that make up the liver. Tissue samples (biopsies) are commonly taken from livers as part of the protocol for research studies on fatty liver disease, and the fat content, degree of inflammation, and fibrous nature of the samples are graded by pathologists, using microscopes and grading criteria, that is, at best, semi-quantitative. The proposed research will develop a technique for rapid and precise automatic quantification of the fat content of liver biopsies, which will be an aid to pathologists for diagnosing fatty liver, and to researchers investigating therapeutics to fatty liver in animal models of the disease.